PYGO1 drives gastric cancer progression via the ITGB1/CD47 axis and is therapeutically targeted by pentagalloylglucose

Abstract

Background

Gastric cancer (GC) represents a significant therapeutic challenge due to its aggressive progression and limited treatment options, emphasizing the urgent need for novel therapeutic targets and strategies. Although PYGO1 functions as a Wnt co-transcriptional activator and chromatin effector, its role in cancer remains poorly characterized. This study aims to elucidate the role of PYGO1 in GC and uncover its regulatory mechanisms.

Methods

Bioinformatics analysis and immunohistochemistry were used to assess PYGO1 expression in GC tissues and its correlation with prognosis and immune cell infiltration. Cellular and animal models were applied to validate the role of PYGO1 in GC. RNA sequencing, flow cytometry, and immunofluorescence explored the underlying mechanisms. Co-immunoprecipitation coupled with mass spectrometry identified PYGO1-interacting proteins. Molecular docking and molecular dynamics simulations screened and evaluated potential PYGO1 inhibitors.

Results

PYGO1 was significantly overexpressed in GC tissues and positively correlated with M2 macrophage infiltration and adverse prognosis. Its knockdown significantly inhibited GC cell proliferation, migration, and invasion in vitro, and reduced tumor growth and metastasis in vivo. Mechanistically, PYGO1 knockdown impaired cell adhesion and disrupted cytoskeletal integrity in GC cells via downregulation of the ITGB1/CD47 axis, mediated by the interaction of PYGO1 with H3K4me^2/3, rather than BCL9. Pentagalloylglucose (PGG) disrupted the PYGO1-H3K4me^2/3 interaction, suppressing the ITGB1/CD47 axis and GC malignancy.

Conclusions

Our study demonstrates the oncogenic role of PYGO1 in GC and identifies PGG as a potential inhibitor, highlighting the PYGO1/ITGB1/CD47 axis as a promising therapeutic target for GC.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-025-06638-5.

Highlights

• PYGO1 is overexpressed in gastric cancer tissues, and its elevated expression correlates with M2 macrophage infiltration and poor prognosis in gastric cancer patients.

• PYGO1 knockdown impairs focal adhesion and cytoskeletal integrity in gastric cancer cells by downregulating the ITGB1/CD47 axis.

• PYGO1 transcriptionally regulates the ITGB1/CD47 axis through its interaction with H3K4 me^2/3, independent of canonical Wnt signaling.

• Pentagalloylglucose is a potential inhibitor of PYGO1 that suppresses ITGB1/CD47 axis transcription by disrupting the PYGO1-H3K4 me^2/3 interaction.

Background

Gastric cancer (GC) remains a major global public health challenge, with epidemiological data from Global Cancer Statistics 2020 documenting over 478,000 new cases and 373,000 deaths annually in China alone [1]. Northwest China exhibits particularly high incidence rates especially in Gansu Province, which stands out as a high-risk region [2]. Although advancements in chemotherapy, targeted therapies including anti-HER2 agents and immune checkpoint inhibitors have improved outcomes in molecularly defined subgroups [3, 4], metastatic GC outcomes remain poor with low five-year survival rates [5]. Current treatments face significant limitations including restricted target applicability, rapid development of therapeutic resistance, and immune-related toxicities [6–8]. This therapeutic trilemma underscores the urgent need to identify novel molecular targets capable of simultaneously suppressing tumor progression and reprogramming the tumor immune microenvironment (TIME), which could provide a dual therapeutic advantage by directly inhibiting tumor growth while enhancing immune-mediated responses, potentially overcoming the limitations of existing treatment approaches.

The Wnt co-transcriptional activator Pygopus (PYGO) serves as a crucial epigenetic regulator. It contains two evolutionarily conserved domains, an N-terminal homology domain (NHD) and a C-terminal plant homeodomain (PHD) [9, 10]. The PHD domain specifically binds to histone H3 lysine 4 di-/trimethylation (H3 K4 me^2/3), facilitating the recruitment of chromatin remodelers to modulate gene expression [11]. Simultaneously, this domain mediates Wnt signaling activation through BCL9 interaction and β-catenin recruitment [12]. This dual functionality positions PYGO as a pivotal molecular hub that integrates Wnt signaling with epigenetic regulation to drive oncogenesis and tumor progression.

Mammalian genomes encode two functionally distinct PYGO subtypes. PYGO2 demonstrates ubiquitous expression and has been extensively characterized as an oncogenic driver in various malignancies [13–15]. PYGO1 exhibits restricted tissue distribution with predominant expression in cardiac tissue. Under physiological conditions, PYGO1 maintains normal cardiac function as demonstrated by multiple studies [16, 17]. However, elevated PYGO1 expression has been shown to induce pathological cardiac hypertrophy [18], revealing its context-dependent functional duality in disease. Recent research has revealed PYGO involvement in diverse biological processes extending beyond Wnt signaling to include metabolic regulation, immune modulation and post-transcriptional control [19]. Despite these advances, the specific role of PYGO1 in cancer remains poorly understood and requires further investigation.

Our findings demonstrate that PYGO1 overexpression correlates with poor clinical outcomes and increased M2 macrophage infiltration in GC patients. Mechanistic investigations reveal that PYGO1 knockdown effectively attenuates GC progression through transcriptional suppression of the integrin β1 (ITGB1)/cluster of differentiation 47 (CD47) axis. Importantly, we identified pentagalloylglucose (PGG) as a potential PYGO1-targeting inhibitor that disrupts PYGO1 binding to H3 K4 me^2/3 and downregulates ITGB1/CD47 expression. These results not only reveal the oncogenic role of PYGO1 in GC but also provide a promising targeted therapeutic strategy for clinical intervention.

Materials and methods

Dataset collection and bioinformatic analysis

Gene expression count data for the Stomach Adenocarcinoma cohort of The Cancer Genome Atlas (TCGA-STAD) were obtained from the TCGA database (http://portal.gdc.cancer.gov/). Moreover, several datasets were retrieved from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), including ACRG/GSE62254 (n = 300), GSE26942 (n = 217), GSE54129 (n = 132), GSE66222 (n = 100), and a single-cell sequencing dataset (GSE163558). The GSE66222 dataset was merged with GSE26942 and GSE54129 to create two new datasets, named GEO1 and GEO2, respectively. Differential gene expression analysis and survival analysis were performed using online platforms (https://www.home-for-researchers.com/#/; https://www.bioinformatics.com.cn/; https://www.xiantaozi.com/). Multivariate Cox regression analysis was conducted using R packages'survival'and'survminer'. Single-gene stratification and functional enrichment analyses were employed R packages including'limma','pheatmap','clusterProfiler','org.Hs.eg.db','enrichplot','ggplot2','circlize','RColorBrewer','dplyr', and'ComplexHeatmap', with the median PYGO1 expression as the cutoff. The Venn diagram was generated using the online tool (https://www.omicsolution.com/wkomics/main/). Correlation analysis was conducted using GEPIA (http://gepia.cancer-pku.cn/), and immune infiltration analysis was provided by the TIMER2.0 (http://timer.cistrome.org/). Single-cell RNA sequencing (scRNA-seq) data were analyzed using the R packages'Seurat'. The marker genes for epithelial tumor cells and M2 macrophages were derived from the CellMarker database (http://xteam.xbio. top/CellMarker/). Cell–cell contact interactions were analyzed using the R package ‘CellChat’.

Tissue microarray and clinical specimens of GC

The Gansu Gastric Cancer (GSGC) cohort was established by collecting samples from GC patients treated at the Affiliated Hospital of Gansu University of Chinese Medicine and Gansu Provincial Hospital (Gansu, China) between January 2015 and December 2023. A total of 170 gastric adenocarcinoma samples and 100 adjacent non-cancerous tissue samples were used to construct a GC tissue microarray for immunohistochemistry (IHC). Follow-up records were maintained for all patients. Additionally, 28 fresh paired GC and adjacent tissue samples were analyzed using western blot (WB), and 4 paraffin-embedded paired samples of GC tissues, adjacent tissues, and lymph node metastasis tissues were subjected to IHC.

Cell culture

Human GC cell lines (MKN45, AGS, HGC27) and HEK293 FT cells were obtained from the National Infrastructure of Cell Resource at the Chinese Academy of Medical Sciences (http://crcpumc.com/) and the China Center for Type Culture Collection at Wuhan University (http://cctcc.whu.edu.cn). MKN28 and SGC7901 cell lines were generously supplied by the Gansu University Key Laboratory for Molecular Medicine and the Chinese Medicine Prevention and Treatment of Major Diseases. The HeLa cell line was kindly gifted by the Shanghai Institute of Immunity and Infection of the Chinese Academy of Sciences. The human gastric epithelial cell line-1 (GES-1) was obtained from Servicebio Technology (Wuhan, China). All cell lines were maintained in a humidified incubator at 37 °C with 5% CO₂. GC cell lines and GES-1 cells were cultured in RPMI-1640 medium (Meilunbio^®, Dalian, China), while HEK293 FT and HeLa cells were cultured in DMEM medium (Meilunbio^®, Dalian, China). The media were supplemented with 10% fetal bovine serum (FBS, PWL001, Meilunbio^®, Dalian, China), 100 U/mL penicillin, and 100 U/mL streptomycin (E607063-0100, BBI, Shanghai, China).

Plasmids and stable cell line construction

The plasmids containing psi-LVRU6GP-shRNAs targeting PYGO1 and control plasmids were obtained from GeneCopoeia (Guangzhou, China). Lentivirus was produced in HEK293 FT cells following the Lenti-Pac™ HIV Expression Packaging Kit protocol (LT001, GeneCopoeia). Lentiviral particle supernatants were harvested 48–72 h post-transfection, filtered using a 0.45 μm filter to eliminate cellular debris, and stored in aliquots at − 80 °C. Target cells were infected with the filtered supernatants, and stable knockdown cell lines were established by selecting these cells with 1–3 μg/mL puromycin (P8230, Solarbio, China) over a 2-week period. The knockdown efficiency of PYGO1 in stable cell lines was evaluated using quantitative real-time polymerase chain reaction (qRT-PCR) and WB.

siRNA transfection

The siRNAs targeting ITGB1 and BCL9 were purchased from TianyiHuiyuan (Beijing, China) and transfected into cells using Lipofectamine 3000 (Invitrogen, NY, USA) following the manufacturer's protocol. The siRNA sequences are listed in Table S1.

Foci formation assay

Cells were plated at a density of 1000 cells/well for HGC27 and 1500 cells/well for MKN45 in 6-well plates and cultured for 10–15 days. Colonies exceeding 50 cells were fixed with 4% paraformaldehyde (AR1068, Boster, China), stained with 1% crystal violet for 10 min (G1062, Solarbio, China), and subsequently counted.

Wound healing assay

Cells were digested and cultured in 10% FBS complete medium in 6-well plates until nearly confluent. A scratch was made with a 200 μL pipette tip, and cells were cultured in FBS-free medium. Images were captured at 0, 24, and 48 h using an Olympus IX53 + DP73 microscope at 100 × magnification.

Cell migration and invasion assay

A total 2 × 10⁴ cells in FBS-free medium were placed into the upper chamber of a 24-well transwell insert (3422, Corning, USA) with or without matrigel coating (0827045, ABW, China). The lower chamber contained 700 μL of medium with 20% FBS. After 30 h, migrated and invaded cells were fixed with 4% paraformaldehyde and stained with 1% crystal violet. Images were captured at 200 × magnification using an Olympus IX53 microscope equipped with a DP73 camera, with six different fields per well being imaged.

qRT-PCR

Total RNA was extracted from cells using TRIzol Reagent (Invitrogen, NY, USA). Reverse transcription was performed using the PrimeScript™ RT Reagent Kit with gDNA Eraser (Takara, Japan). qRT-PCR was conducted using SYBR Premix Ex Taq™ II (Tli RNaseH Plus, Takara, Japan) on a QuantStudio Dx system (Thermo Fisher, USA), with GAPDH as the reference gene. Relative expression levels were calculated using the ΔΔCt method. Primer sequences are provided in Table S2.

WB assay

Total protein was extracted using RIPA buffer with protease and phosphatase inhibitors (KGP2100, KeyGEN). Proteins were separated by 10% SDS-PAGE (AR0047, BOSTER) and transferred to PVDF membranes (IPVH00010, Merck Millipore). The membranes were blocked with Protein-Free Rapid Blocking Solution (AR0041, BOSTER) and incubated with primary antibodies overnight at 4℃, followed by secondary antibodies for 1 h at room temperature (RT). Protein bands were visualized using an ECL reagent (P10100, NCM) and imaged with an e-Blot Touch Imager (e-BLOT). Primary antibodies included anti-PYGO1 (1:3000, ab95072, Abcam), GAPDH (1:5000, AF7021, Affinity), anti-ITGB1(1:1000, GB115173-50, Servicebio), anti-Src (1:1000, ET1702-03, HUABIO), anti-pSrc(Y419) (1:1000, ET1609-15, HUABIO), anti-FAK (1:1000, ET1602-25, HUABIO), anti-pFAK (Y397) (1:1000, 8556 T, CST), anti-Paxillin (1:1000, 10,029–1-Ig, Proteintech), anti-pPaxillin (Y118) (1:1000, R381444, Zenbio), and anti-BCL9 (1:1000, NBP3-05612, Novus). Secondary antibodies were goat anti-rabbit IgG (1:10,000, GB23303, Servicebio) and goat anti-mouse IgG (1:5000, S0002, Affinity), diluted in WB-specific antibody diluent (AR1017, BOSTER).

IHC staining

The paraffin-embedded tissue sections (4 μm) were incubated at 70 °C for 1 h and stained using an IHC SP kit (PV-6001, ZSGB Bio) according to the manufacturer's protocol. Images were captured using an Olympus microscope (BX53 + DP73, Japan) and a slide scanner (IMD-Neo-5X, InteMedic, Guangzhou). IHC scoring was based on staining intensity (0–3: none to strong) and the percentage of positively stained cells (0–4: 0% to 100%). The final score (0–12) was calculated by multiplying the intensity and area scores. Two blinded pathologists independently evaluated all slides to ensure objectivity. Primary antibodies included anti-PYGO1 (1:200, NBP1-42,665, Novus), anti-CD163 (ZM0428, ZSGB-BIO), and anti-CD47 (1:200, 20,305–1-AP, Proteintech).

Cell viability assay

HGC27 and MKN45 cells were seeded in 96-well plates (Corning, USA) at densities of 2 × 10³ and 5 × 10³ cells/well, respectively, using 100 μL of medium per well. After overnight incubation at 37 °C with 5% CO₂, pentagalloylglucose (PGG, Biopurify China) was added at various concentrations. Subsequently, 10 μL of CCK-8 reagent (K1018, Apexbio) was added to each well, and the plates were incubated for 2 h at 37 °C with 5% CO₂. Optical density was measured at 450 nm using a microplate reader (BioTek EPOCH, USA). All experiments were conducted in triplicate, and the half-maximal inhibitory concentration (IC50) values of PGG were calculated using GraphPad Prism 9.

EdU assay

GC cells were cultured in 6-well plates for the EdU-555 assay (MA0425-1, MeilunBio^®, Dalian, China). Cells were treated with 1 × EdU buffer for 2 h, followed by a 30 min staining with Apollo^®555, and were subsequently analyzed via flow cytometry (FCM, BD FACSAria II, USA) using FlowJo (Version 10.8.1).

Cell apoptosis

GC cells and supernatants were collected and stained using the Annexin V-APC/7-AAD double-staining kit (70-AP105-60, MULTI SCIENCES, China). Apoptosis was assessed via using FCM and FlowJo. Apoptosis of PGG-treated cells for 24 h was similarly stained and tested.

Intracellular calcium assay

Cells were collected and stained using the Fluo-4 Calcium Assay Kit (S1061S, Beyotime) for 30 min and analyzed by FCM and FlowJo.

CD47 detection

Cells were collected and stained with anti-CD47-APC (B6H12, 17–0479-41, Invitrogen, USA) and isotype control (17–4714-81, Invitrogen, USA) for 30 min, and analyzed using FCM and FlowJo. PGG-treated cells were processed using the same staining procedure.

RNA sequencing (RNA-seq)

RNA extraction was conducted using the Total RNA Isolation Kit (Thermo Fisher, Cat. No. 15596018). The quantity and quality of the extracted RNA were evaluated using an Agilent Bioanalyzer 2100 and the RNA 6000 Nano LabChip Kit (Agilent, CA, USA, Cat. No. 5067–1511). Library preparation was conducted on RNA samples with a RIN score exceeding 7.0. cDNA was synthesized utilizing SuperScript™ II Reverse Transcriptase (Cat. No. 18064014, CA, USA). RNA sequencing was performed using the Illumina NovaSeq™ 6000 platform by LC Bio Technology in China. Differential gene expression (DEG) analysis was performed using the DESeq2 package, with criteria of |Fold Change|≥ 1 and Q-value < 0.05. Bioinformatics and visualization were conducted using OmicStudio (https://www.omicstudio.cn/tool).

Immunofluorescence (IF) staining

GC cells were grown in confocal dishes (801,001, NEST) coated with 100 ng/mL poly-L-lysine (P2100, Solarbio, China). The samples were fixed with 4% paraformaldehyde for 30 min at RT, permeabilized with 0.5% Triton X-100 for 10 min, and blocked with 4% bovine serum albumin for 30 min. The samples were then incubated overnight at 4℃ with anti-PYGO1 antibodies (1:100, NBP1-86,218, Novus). After washing, the slides were incubated with Cy3-conjugated secondary antibodies (1:100, SA00009-2, Proteintech) and FITC-phalloidin (1:200, 40735ES75, Yeasen) for 30 min, and counterstained with 4',6-diamidino-2- phenylindole (DAPI, Solarbio, China). Images were captured using a Leica SP8 confocal laser microscope (Leica, Germany).

Animal experiments

Four-week-old male BALB/c nude mice (GemPharmatech Co., Ltd., Jiangsu, China) were used to assess xenograft growth. MKN45-sh1 cells (PYGO1-knockdown) or MKN45-scr cells (Control), each at a concentration of 1 × 10⁶ cells in 150 µL phosphate-buffered saline, were injected into the right axilla of the mice (n = 5 per group). Tumor volume was measured every four days using the formula: volume = length × width² × 0.5. After three weeks, the mice were anesthetized with avertin (M2910, Nanjing, China) and imaged using a small animal in vivo imaging system (AniView100, BLT, China). Subsequently, all mice were euthanized, and their tumor tissues were promptly frozen in liquid nitrogen for subsequent analysis. For metastasis studies, identical cells were injected into the right abdominal area of the mice (n = 3 per group). After 6–7 weeks, the mice were euthanized, and their liver samples were fixed in 4% paraformaldehyde for H&E staining.

Immunoprecipitation (IP) and Mass spectrometry (MS)

HeLa cells expressing Flag-PYGO1 were harvested and lysed in IP lysis buffer supplemented with a protease inhibitor (KGP2100, KeyGEN). After centrifugation, the supernatants were incubated with anti-FLAG^® M2 Magnetic Beads (M8823, Sigma-Aldrich) overnight at 4℃. The beads were washed, and the complexes were examined using WB and liquid chromatography-tandem mass spectrometry (LC–MS/MS) with an EASY-nLC1200 system linked to a Q Exactive mass spectrometer (Thermo Fisher Scientific, USA). The raw data were analyzed and visualized using MaxQuant v2.6.7.0 (https://www.maxquant.org/) and Proteome Discoverer™ 2.4 (Thermo Fisher Scientific, USA).

Molecular docking

The crystal structure of PYGO1 (PDB ID: 2 VPB) was obtained from the Protein Data Bank (RCSB PDB; https://www.rcsb.org/) for molecular docking studies. A small molecule library derived from the Guiqi Baizhu formula, previously compiled by our research team, was used. The structures of these molecules were preprocessed using the Prep Wiz module from Schrödinger software (version 2020–2(32)). The binding pocket of PYGO1 was identified based on previously published literature [20]. The docking procedure was adapted from previous research [21] and is described as follows: Low-energy conformations of the molecules were generated using LigPrep with the Merck Molecular Force Field (MMFFs), and ionization states were determined by Epik at a pH range of 7.0 ± 2.0. Docking simulations were performed using the Glide XP mode. Finally, the binding free energy between the ligand and receptor was calculated using the Prime MM-GBSA module from Schrödinger software.

Molecular dynamics simulation

GROMACS 2020.6 was used for 50 ns molecular dynamics (MD) simulations to analyze PGG-PYGO1 complex interactions. Protein topology was built with AMBER99SB-ILDN, and ligand topology with Amber20 and GAFF. Stable conformations from the last 20 ns were extracted to study protein–ligand interactions. The most stable conformation was visualized in Discovery Studio.

Statistical analysis

The statistical analysis was performed using GraphPad Prism Software (version 9). Data are expressed as mean ± standard deviation (SD). Comparisons between two groups were conducted using either a Student's t-test or a Wilcoxon rank-sum test. One-way ANOVA was employed to assess statistical differences among multiple treatment groups. Pearson or Spearman correlation analysis was used to calculate the correlation coefficient. Kaplan–Meier methods and log-rank tests were utilized for survival analysis. P < 0.05 was considered to indicate statistical significance.

Results

Elevated PYGO1 expression in GC tissues is associated with adverse outcomes in patients with GC

To examine the clinical significance of PYGO1 in GC, we analyzed its expression in GC tissues and assessed its association with patient outcomes. PYGO1 expression was significantly higher in GC tissues than in adjacent non-tumor tissues in both the GEO1 and GEO2 datasets (P < 0.0001, Fig. 1A). Multivariate Cox regression analysis identified PYGO1 as an independent prognostic factor for GC, with significant hazard ratios (HR) in the TCGA (HR = 1.707; 95% CI 1.255–2.323, P < 0.001) and GSE62254 (HR = 1.425; 95% CI 1.168–1.740, P < 0.001) cohorts (Fig. 1B). Patients with high PYGO1 expression exhibited significantly worse overall survival (OS) and disease-free survival (DFS) than those with low expression (Fig. 1C). Furthermore, PYGO1 expression increased progressively with tumor stage, showing significantly higher levels in stages II (P = 0.0073), III (P = 0.004), and IV (P = 0.022) compared to stage I (Fig. 1D).

Fig. 1.

PYGO1 is overexpressed in GC and correlated with poor prognosis. A The relative mRNA levels of PYGO1 were compared between GC tissues and adjacent non-tumor tissues using the GEO1 (n = 317) and GEO2 (n = 232) datasets. B Multivariate regression analysis was conducted to evaluate the prognostic significance of PYGO1 in the TCGA-STAD and GSE62254 cohorts. C Kaplan–Meier analysis was performed to assess OS and DFS in TCGA-STAD and GSE62254 cohorts based on PYGO1 expression levels. D PYGO1 expression was compared across different tumor stages within the TCGA-STAD dataset. E PYGO1 expression was evaluated in human GC samples using WB (n = 8). F Relative quantification of PYGO1 expression (Fig.E) was normalized to GAPDH. G Representative images of PYGO1 expression by IHC in GC tissue microarray from the GSGC cohort. H IHC scores for PYGO1 were compared between GC tissues (n = 170) and adjacent normal tissues (n = 100). I Kaplan–Meier survival analysis of OS in GSGC cohort based on PYGO1 expression. J Representative images of PYGO1 expression in paired adjacent normal tissue, GC tissue and metastatic lymph node tissue. Data are presented as the mean ± SD. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001. GC, Gastric cancer. OS, overall survival. DFS, Disease-free survival. WB, Western blot. GSGC cohort, Gansu Gastric Cancer cohort

The upregulation of PYGO1 was further validated in paired GC tissues and adjacent normal tissues by WB (Fig. 1E, F). These results were corroborated in the GSGC cohort, where PYGO1 was highly expressed in GC tissues and strongly associated with poor prognosis (Fig. 1G–I). PYGO1 emerged as an independent prognostic marker for GC (HR = 2.147; 95% CI 1.164–3.959, P = 0.014, Table 1). Among four pairs of matched GC tissues, peritumoral tissues, and metastatic lymph node tissues, PYGO1 overexpression was observed in one pair of metastatic lymph node tissues (Fig. 1J). Taken together, these findings suggest that PYGO1 serves as a prognostic biomarker and plays a critical role in GC progression and metastasis.

Table 1.

Univariate and multivariate Cox regression analyses of prognostic value of PYGO1 expression in the GSGC cohort

Clinicopathological features	Univariate analysis		Multivariate analysis
	HR(95% CI)	P	HR(95% CI)	P
Sex (female vs. male)	0.444 (0.175–1.127)	0.088
Age (> 60 vs. ≤ 60)	1.852 (1.058–3.244)	0.031	1.982 (1.139–3.451)	0.016
Differentiation (low + middle vs. high)	1.060 (0.588–1.911)	0.847
T	2.007 (1.101–3.659)	0.023	2.228 (1.270–3.908)	0.005
N	1.497 (1.159–1.932)	0.002	1.461 (1.142–1.868)	0.003
M	0.692 (0.235–2.042)	0.505
PYGO1	2.215 (1.188–4.129)	0.012	2.147 (1.164–3.959)	0.014

HR:Hazard ratio

PYGO1 overexpression positively correlates with M2 macrophage infiltration

To investigate the correlation between PYGO1 expression and the TIME, we analyzed its association with immune cell infiltration levels using the TCGA-STAD dataset. Our results revealed significant positive correlations between PYGO1 expression and both M2 macrophage infiltration (P < 0.001) and regulatory T cell infiltration (P = 0.018), along with a negative correlation with M1 macrophage infiltration (P = 0.005, Fig. 2A). These findings were further validated using multiple computational algorithms, including EPIC, TIMER, and CIBERSORT (Fig. S1). Of clinical relevance, survival analysis revealed that patients with co-elevated levels of PYGO1 and M2 macrophage exhibited significantly poorer prognosis (P = 0.003, Fig. 2B). scRNA-seq data from GC (GSE163558) identified 14 distinct cell clusters (Fig. 2C). Among these, clusters 4, 7, 9, 10, 11, 12, 13, and 14 were classified as epithelial tumor cells, based on the expression of marker genes such as KRT19, CLDN4, and EPCAM. These epithelial clusters were further divided into high-PYGO1 (clusters 11, 12, and 13) and low-PYGO1 (clusters 4, 7, 9, 10, and 14) subgroups. Cluster 6 was identified as an M2 macrophage population characterized by the expression of CD163, MRC1, and CD206 (Fig. 2D). Cell–cell interaction analysis demonstrated that the crosstalk between PYGO1-high tumor cells and M2 macrophages was significantly stronger than that between PYGO1-low tumor cells and M2 macrophages (Fig. 2E).

Fig. 2.

Elevated PYGO1 expression is positvely associated with M2 macrophage infiltration. A Spearman correlation analysis of PYGO1 expression and the infiltration levels of various immune cell types in the TCGA-STAD cohort. B Kaplan–Meier survival analysis of OS based on PYGO1 expression and M2 macrophage levels. C PYGO1 expression was positviely correlated to CD163 expression using the GEPIA database. D PYGO1expression was positviely correlated to CD47 expression using the GEPIA database. E Single-cell transcriptome analysis of 3 primary GC tissue, 2 GC with lymph nod metastasis, 2 GC with liver metastasis, 1 GC with peritoneum metastasis from GSE 163558, identified 14 distinct cell clusters. F Violin plots illustrate the expression distribution of marker genes in tumor epithelial cell clusters (KRT19, CLDN4 and EPCAM), PYGO1 and CD47 expression clusters, and M2 macrophage clusters (CD163, MACRO and MRC1). G Cell–cell contact interactions analysis of the strength of interactions between tumor cells with high and low PYGO1 expression and M2 macrophages. H The proportions of patients with low and high CD163 expression were compared between those with low and high PYGO1 expression in the GSGC cohort. I The proportions of patients with high and low CD47 expression were compared between those with low and high PYGO1 expression in the GSGC cohort. J. Representative IHC images depict the expression levels of PYGO1, CD163, and CD47 in patients from the GSGC cohort. GC, Gastric Cancer. IHC, Immunohistochemistry. GSGC cohort, Gansu Gastric Cancer cohort

To further elucidate the underlying mechanisms between PYGO1 and M2 macrophage polarization, we analyzed its association with key M2 macrophage markers CD163 (a canonical M2 marker) and CD47 (a therapeutic target in macrophage-directed therapies). Our data demonstrated strong positive correlations between PYGO1 expression and both CD163 (r = 0.35, P = 3.0 × 10⁻¹³, Fig. 2F) and CD47 (r = 0.21, P = 1.6 × 10⁻⁵, Fig. 2G). These findings were independently confirmed in the GSGC cohort, where PYGO1 expression showed significant positive correlations with CD163 (r = 0.251, P < 0.01) and CD47 (r = 0.335, P < 0.001, Table 2).

Table 2.

The association between PYGO1 expression and both CD163 and CD47 in the GSGC cohort (n = 170)

	CD163low	CD163 high	r	P	CD47low	CD47 high	r	P
PYGO1low	51(65.4)	27(34.6)	0.251	< 0.01	49(65.4)	29(34.6)	0.335	< 0.001
PYGO1high	37(40.2)	55(59.8)			27(40.2)	65(59.8)

IHC analysis revealed a significantly greater proportion of patients with elevated CD163 and CD47 in the high-PYGO1 group compared to the low-PYGO1 group (Fig. 2H–I). Representative IHC images from matched samples are shown in Fig. 2J. Together, these multi-omics analyses suggest that PYGO1 overexpression fosters an immunosuppressive tumor microenvironment by enhancing M2 macrophage infiltration and facilitating their interaction with tumor cells.

PYGO1 downregulation inhibits GC growth and metastasis

To elucidate the functional role of PYGO1 in GC, we conducted a series of in vitro and in vivo experiments. Initially, we screened five GC cell lines and identified HGC27 and MKN45 as exhibiting relatively high PYGO1 expression using qRT-PCR and WB (Fig. 3A). Subsequently, we established stable PYGO1-knockdown cell lines in the selected HGC27 and MKN45 models using lentiviral transduction of PYGO1-targeting shRNAs. The efficiency of PYGO1 knockdown was confirmed by qRT-PCR and WB, showing a significant reduction in PYGO1 mRNA and protein levels (Fig. 3B). The EdU assay revealed that PYGO1 knockdown significantly decreased the number of EdU-positive cells compared to the control group (Fig. 3C). Similarly, the colony formation assay showed a marked reduction in the number of colonies formed by PYGO1-knockdown cells (Fig. 3D). In xenograft models, PYGO1 knockdown significantly suppressed tumor growth. Compared to the control (MKN45-scr) cells, MKN45-sh1 (PYGO1-knockdown) cells formed smaller tumors with reduced volumes and weights (Fig. 3E–H).

Fig. 3.

Knockdown of PYGO1 inhibits tumor growth and metastasis both in vitro and in vivo. A The relative mRNA and protein levels of PYGO1were assessed in GES1 and five GC cell lines using qRT-PCR and WB. B The relative mRNA and protein levels of PYGO1were evaluated in HGC27 and MKN45 cells following PYGO1 knockdown using qRT-PCR and WB. C Cell proliferation was compared between PYGO1 knockdown GC cells (sh1) and control cells (scr) using the EdU method and FCM. D Colony formation capacity was compared between PYGO1 knockdown cells and control cells. E Bioluminescent imaging of tumor-bearing nude mice treated with PYGO1-knockdown MKN45 cells and control cells subcutaneously (n = 5). F Images of xenografts formed in nude mice (n = 5). G–H Tumor growth curves and tumor weights were compared between the MKN45-sh1 group and the MKN45-scr group (n = 5). I Transwell assays were performed to compare the capacity of migration and invasion (with or without Matrigel) between HGC27-sh1 cells and HGC27-scr cells (× 200). J. Wound healing assays were conducted to compare migration between HGC27-sh1 cells and HGC27-scr cells (× 200). K HE stain image of metastatic liver tissue between MKN45-sh1 group and MKN45-scr group (n = 3). Data are presented as the mean ± SD. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. WB, Western blot. qRT-PCR, Quantitative real-time polymerase chain reaction. FCM, flow cytometry

Furthermore, transwell and wound healing assays demonstrated that PYGO1 inhibition significantly suppressed the invasion and migration capabilities of GC cells (P < 0.0001, Fig. 3I–J). Notably, similar results were observed in the PYGO1-knockdown SGC7901 cell line (Fig. S2 A-C). We further confirmed these findings in vivo, showing that PYGO1 knockdown in MKN45-sh1 group decreased distant metastasis, especially liver metastasis, compared to the MKN45-scr group (Fig. 3K). Collectively, these results establish PYGO1 as a critical oncogenic driver that promotes GC progression and metastasis, suggesting its potential as a therapeutic target.

PYGO1 reduction impairs focal adhesion and extracellular matrix (ECM) -receptor interaction by dysregulating the actin cytoskeleton

To clarify the molecular mechanisms underlying PYGO1 function on GC growth and metastasis, we performed RNA-seq on HGC27 stable cell lines with PYGO1 knockdown, which identified a total of 1002 DEGs, comprising 531 upregulated and 471 downregulated genes (Table S3). KEGG enrichment analysis revealed 14 significantly enriched pathways (P < 0.05, Fig. 4A). Stratification analysis of the TCGA-STAD dataset based on the median expression level of PYGO1 highlighted 41 significantly enriched KEGG pathways (P < 0.05, Fig. 4B). Pathway intersection analysis demonstrated that ECM-receptor interaction and focal adhesion were the most significantly affected by PYGO1 knockdown (Fig. 4C, D). Concurrently, GO enrichment analysis identified 124 significantly enriched pathways in the RNA-seq data and 885 enriched pathways in the TCGA dataset (P < 0.05). The intersection of these enriched pathways revealed 40 overlapping pathways (Fig. 4E–G). The top enriched GO terms included integrin-mediated cell adhesion (GO:0033627, biological processes), collagen-containing extracellular matrix (GO:0062023, cellular components), and extracellular matrix structural constituents (GO:0005201, molecular functions)(Fig. 4H). Furthermore, GSEA analysis confirmed the role of integrin functions in cell adhesion (Fig. 4I).

Fig. 4.

Decreased PYGO1 levels disrupt focal adhesion and ECM-receptor interaction through actin cytoskeleton dysregulation. A KEGG pathway enrichment for various biological categories based on DEGs from RNA-seq data of HGC27 cell with or without PYGO1-knockdown. B Top 20 KEGG enrichment pathway based on DEGs from the RNA-seq data (left) and from the TCGA-STAD database (right). C Venn diagram illustrates the overlap of KEGG-enriched pathways between the two datasets. D The top two common enriched KEGG pathways are"ECM-receptor interaction"(hsa04512) and"Focal adhesion"(hsa04510). E GO pathway enrichment for various biological categories based on DEGs from the RNA-seq data. F Top 20 GO enrichment pathway based on DEGs from the RNA-seq data (left) and the TCGA-STAD database (right). G Venn diagram exhibits the overlap of GO-enriched pathways between the two datasets. H Image exhibits the most significantly overlapping GO terms between the two datasets, showing the top enriched terms in biological process, cellular component, and molecular function. I GSEA analysis was performed on RNA-seq data using pre-ranked gene lists with Hallmark gene sets. J The integrity of the cytoskeleton in GC cells with or without PYGO1 knockdown was evaluated by IF assays using confocal microscopy. Scale bars, 5 μm. K Intracellular calcium ion concentrations were detected using FCM. *P < 0.05; **P < 0.01; ***P <0.001; ****P < 0.0001. DEGs, Differentially expressed genes. ECM, Extracellular matrix. IF, Immunofluorescence. MFI, Mean fluorescence intensity. FCM, flow cytometry

To evaluate the role of PYGO1 in cytoskeletal integrity, IF staining was conducted. Results showed that control cells displayed intact cytoskeletons with well-organized F-actin filaments, whereas PYGO1-knockdown cells exhibited disrupted actin networks, characterized by disorganized and tangled actin filaments (Fig. 4J, Fig. S4 A). Additionally, PYGO1 knockdown induced dysregulation of intracellular calcium homeostasis. A pronounced increase in intracellular calcium ion concentration was observed in PYGO1-knockdown cells compared to control cells (Fig. 4K). Furthermore, we evaluated the adhesion capacity of PYGO1-knockdown cells under standard culture conditions. After 4 h of culture, fewer PYGO1-knockdown cells (HGC27-sh1) adhered to the bottom of the culture plate compared to the control cells (HGC27-scr) that had already adhered. Similar results were observed in MKN45 knockdown cells after 24 h of incubation (Fig. S4B). Collectively, these data indicate that PYGO1 downregulation leads to significant disruptions in cytoskeletal organization, attenuates cell adhesion, and dysregulates intracellular calcium homeostasis.

PYGO1 knockdown transcriptionally represses the ITGB1/CD47 axis

Building upon RNA-seq and GSEA analysis results, our comprehensive approach revealed that integrin-mediated signaling components were downregulated following PYGO1 knockdown. qRT-PCR, WB, and FCM analyses confirmed significant reductions in ITGB1 and CD47 expression at mRNA and protein levels in PYGO1-knockdown cells compared to control cells (Fig. 5A, B, D, E). Bioinformatic analysis using GEPIA demonstrated a strong positive correlation between PYGO1 and ITGB1 expression (R = 0.69, P < 0.001, Fig. 5C). Mechanistically, PYGO1 inhibition significantly reduced phosphorylation levels in the canonical SRC/FAK/PXN signaling cascade (Fig. 5F). Furthermore, GEPIA analysis uncovered a significant positive correlation between ITGB1 and CD47 expression (R = 0.43, P < 0.001; Fig. 5G). siRNA-mediated ITGB1 knockdown directly reduced CD47 expression (Fig. 5H–K), functionally validating this regulatory relationship between these two proteins. Together, these findings demonstrate that PYGO1 critically regulates cell adhesion in GC by modulating the ITGB1/CD47 axis and SRC/FAK/PXN signaling pathway.

Fig. 5.

Knockdown of PYGO1 inhibits the transcriptional expression of the ITGB1/CD47 axis. A, B The expression levels of ITGB1 in GC cells with or without PYGO1 knockdown were evaluated by qRT-PCR and WB. C The correlation between PYGO1 and ITGB1 expression in GC tissues was analyzed using the GEPIA database. D, E The expression of CD47 in GC cells with or without PYGO1 knockdown was assessed by qRT-PCR and FCM. F The expression levels of pSRC-Y419, SRC, pFAK-Y397, FAK, and pPXN-Y118 in GC cells with or without PYGO1 knockdown were determined by WB. G The correlation between ITGB1 and CD47 expression in GC tissues was analyzed using the GEPIA database. H, I The expression of ITGB1 in GC cells with or without ITGB1 knockdown was evaluated by qRT-PCR and WB. J, K The expression of CD47 in GC cells with or without ITGB1 knockdown was assessed by qRT-PCR and FCM. Data are presented as mean ± SD. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. WB, Western blot. qRT-PCR, Quantitative real-time polymerase chain reaction. FCM, flow cytometry

PYGO1 modulates ITGB1/CD47 axis transcription by interacting with H3K4 me.^2/3

To investigate the molecular mechanisms by which PYGO1 transcriptionally regulates the ITGB1/CD47 axis, we first identified PYGO1-interacting proteins through IP coupled with MS in PYGO1-overexpressing HeLa cells. It is well known that PYGO1 functions as a transcriptional regulator through its interaction with BCL9 and recognition of H3K4 me^2/3 via its PHD domain (Fig. S4 A). Consistent with these reports, our IP-MS analysis confirmed the physical interaction between PYGO1 and BCL9 (Fig. S4B-D). However, siRNA-mediated knockdown of BCL9 failed to suppress either ITGB1 mRNA expression or protein abundance (Fig. S4E-H), indicating that PYGO1 regulates ITGB1 through a BCL9-independent mechanism.

To validate whether PYGO1 regulates ITGB1 transcription through its interaction with H3K4 me^2/3, we initially performed structural modeling of their interaction interface using AlphaFold3. The computational predictions between the PYGO1 PHD domain and H3K4 me^2/3 exhibited high confidence scores, with ipTM scores of 0.82 and 0.85, and pTM scores of 0.45 and 0.49, respectively (Fig. 6A, B). A total of 10 hydrogen bonds were identified between PYGO1 and H3K4 me², while 12 hydrogen bonds were observed between PYGO1 and H3K4 me³. Seven key residues were involved in both interactions including Ala356, Leu358, Glu360, Tyr379, Thr383, Ser387, and Asp393. We further analyzed the interaction interfaces between PYGO1 and BCL9 as well as H3K4 me^2/3. The results showed that the binding surface for H3K4 me^2/3 featured a deeper groove compared to the smoother binding surface for BCL9 (Fig. S4 A). Based on these findings, we selected the groove where PYGO1 binds to H3K4 me^2/3 as the target site for screening potential inhibitors targeting this interaction interface.

Fig. 6.

PYGO1 modulates the transcription of the ITGB1/CD47 axis through its binding to H3K4 me^2/3. A, B Structural models of the interaction between the PYGO1 PHD domain and H3K4 me^2/3 predicted by AlphaFold3. C Molecular docking of PGG with the PYGO1 PHD domain using AutoDock Vina and Schrödinger software. D Three-dimensional (3D) and two-dimensional (2D) visualization of the binding mode between PGG and the PYGO1 PHD domain. E IC50 values of PGG on GC cells at 24 h. F Effects of PGG on the transcription of the ITGB1/CD47 axis in GC cells assessed by qRT-PCR at 24 h. G Effects of PGG on the ITGB1-FAK signaling pathway in GC cells evaluated by WB at 24 h. H Effects of PGG on CD47 expression in GC cells analyzed by FCM at 24 h. I The cytoskeleton integrity in GC cells treated with or without PGG for 24 h was assessed by IF assays using confocal microscopy. Scale bars, 5 μm. Data are presented as the mean ± SD. *P < 0.05; **P < 0.01; ***P < 0.001; ****, P < 0.0001. IF, Immunofluorescence. PHD, Plant Homeodomain. WB, Western blot. PGG, Pentagalloylglucose. qRT-PCR, Quantitative real-time polymerase chain reaction. FCM, flow cytometry

To identify potential inhibitors, we conducted structure-based virtual screening of our in-house anti-tumor compound library, resulting in the selection of 94 small molecules with promising docking scores below −5 (Table S4). Among these, PGG emerged as the candidate compound exhibiting significant anti-tumor activity. Its strong binding affinity for PYGO1 was confirmed through cross-validation using Schrödinger and AutoDock Vina, with docking scores of −11.96 and −8.6, respectively (Fig. 6C). MM-GBSA calculations revealed a highly favorable binding free energy of − 83.327 kcal/mol for the PGG-PYGO1 complex. MD simulations over 50 ns provided additional evidence of a stable complex formation between PGG and PYGO1. The system reached dynamic equilibrium from 20 to 50 ns, as indicated by minimal fluctuations in root-mean-square deviation (RMSD) (Fig. S5 A). Other stability metrics, including radius of gyration (Rg), binding area, and root-mean-square fluctuation (RMSF), remained steady throughout the simulation (Fig. S5B–E). Notably, several key residues, including Tyr341, Gln354, Asp355, Glu376, and Ala386, consistently maintained hydrogen bond interactions with PGG during the simulation (Fig. S5 F-G). Finally, we visualized the stable complex conformations from the MD simulation in 2D and 3D (Fig. 6D), revealing that PGG binds to PYGO1 through a combination of hydrophobic interactions, van der Waals contacts, hydrogen bonds, and π-π stacking interactions. Critical hydrogen bonds with Tyr379, Thr383, Glu385, and Ala386 contributed to the overall stability of the complex.

We then explored whether PGG could disrupt the PYGO1-H3K4 me^2/3 interaction, which might in turn inhibit the transcription of the ITGB1/CD47 axis at the cellular level. Our results indicated that PGG exerted cytotoxic effects on GC cells in a concentration- and time-dependent manner (Fig. 6E). Specifically, the IC50 values for HGC27 cells were 46.03 μM and 14.11 μM at 24 and 48 h, respectively, whereas those for MKN45 cells were 90.79 μM and 21.43 μM at the corresponding time points. PGG significantly decreased cell viability, triggered apoptosis, and suppressed colony formation in GC cells (Fig. S6 A-C). qRT-PCR, WB, and FCM analyses showed that PGG markedly downregulated the expression of both ITGB1 and CD47 at the mRNA and protein levels (Fig. 6F–H). Additionally, PGG disrupted cytoskeletal integrity and caused disorganization of F-actin arrangement in GC cells (Fig. 6I). Taken together, these findings demonstrate that PGG effectively inhibits the transcription of the ITGB1/CD47 axis by disrupting the PYGO1-H3K4 me^2/3 interaction, highlighting its potential as an inhibitor targeting PYGO1.

Discussion

Our study has uncovered PYGO1 as a new and pivotal oncogenic factor in GC. It drives malignant progression by modulating the ITGB1/CD47 axis via non-canonical Wnt signaling. This discovery sheds light on a fresh molecular mechanism controlled by PYGO1 in GC. Clinically significant, we have identified the small-molecule compound PGG as an effective agent that specifically targets the PYGO1-H3K4 me^2/3 interaction, leading to significant suppression of the ITGB1/CD47 axis and marked reduction of malignant phenotypes in GC. These results enhance our comprehension of PYGO1 biology and highlight the PYGO1/ITGB1/CD47 pathway as a potential therapeutic target, paving the way for innovative precision medicine strategies in GC therapy.

Several studies have demonstrated that PYGO regulates transcription through both canonical Wnt/β-catenin signaling and Wnt-independent epigenetic regulation [19, 22, 23]. PYGO1 exhibits bidirectional modulation of cardiac function in a dose-dependent manner. Increased PYGO1 expression induces cardiac hypertrophy through the Wnt/β-catenin pathway [18], whereas PYGO1 deficiency disrupts calcium homeostasis and causes arrhythmias via Wnt-independent pathway [17]. In cancer, PYGO2 primarily drives tumor progression through both Wnt-dependent and Wnt-independent pathways [14, 24]. Our study demonstrates that PYGO1 exerts oncogenic effects through a Wnt-independent pathway in GC. Reduced PYGO1 expression did not significantly affect the canonical Wnt signaling pathway (P = 0.12). It mainly regulates downstream signaling pathways by binding H3K4 me^2/3 through its PHD domain, independent of BCL9 interaction. Of great importance, the calcium homeostasis disruption phenotype induced by PYGO1 knockdown in GC cells closely resembles previously reported findings in cardiac function studies [17], suggesting the conservation of Wnt-independent mechanisms across different tissues. However, given structural evidence of allosteric modulation of interactions between PYGO and H3 K4 me by BCL9 and the capacity of the HD1 domain of BCL9 to enhance H3K4 me binding affinity [20, 25], the potential contributory role of BCL9 in PYGO1-mediated oncogenesis cannot be completely dismissed. Future research should focus on elucidating the dose-dependent regulatory mechanisms of PYGO1 and clarifying the dynamic interplay between Wnt-dependent and Wnt-independent activities of PYGO1 under physiological and pathological conditions to uncover new therapeutic potential.

Integrin-mediated cell adhesion and ECM interactions, along with cytoskeletal remodeling, play critical roles in tumor metastasis [26]. In this study, we provide novel evidence that PYGO1 serves as a novel upstream regulator of the ITGB1/CD47 axis. This study demonstrates that PYGO1 modulates cell adhesion and cytoskeletal organization through the ITGB1/FAK/SRC/PXN signaling pathway, consistent with previous studies that reported the tumor-promoting function mediated by ITGB1 [27, 28]. This regulatory network can be further amplified through CD47-mediated SRC phosphorylation, which coordinately modulates both cell adhesion and migration via dynamic actin cytoskeleton reorganization [29, 30]. CD47 functions not only as an integrin-associated adhesion molecule but also as an immune checkpoint. Clinically, we observed that elevated PYGO1 expression positively correlates with CD47 levels in GC tissues and coincides with enhanced M2 macrophage infiltration, indicating that PYGO1 may promote immune evasion through upregulation of CD47. These results are supported by previous studies demonstrating that CD47 binds ligands like SIRPα to inhibit phagocytosis and induce M2 polarization [31, 32]. Our study further reveals that PYGO1 binding to H3K4 me^2/3regulates expression of ITGB1/CD47 axis. Although H3K4 me³ is known to activate CD47 transcription through promoter binding [33], the epigenetic mechanisms by which PYGO1 coordinates simultaneous ITGB1/CD47 regulation via histone modification crosstalk and chromatin remodeling remain to be fully elucidated regulation via histone modifications and chromatin remodeling.

ITGB1 and CD47 have emerged as promising yet challenging therapeutic targets for cancer treatment. Clinical studies have demonstrated that these molecules are frequently upregulated in GC and other malignancies, with their elevated expression showing strong correlation with poor patient outcomes [34–36]. However, current treatment strategies targeting these molecules face substantial limitations. Integrin inhibitors may unexpectedly activate integrin function, increasing the risks of thrombosis and thrombocytopenia [37]. Similarly, CD47 blocking agents often cause hematologic toxicities including anemia and thrombocytopenia, resulting from the ubiquitous expression of CD47 in normal tissues, especially on erythrocytes [38]. Given these limitations, PYGO1 represents a promising alternative upstream target. In contrast to ITGB1 and CD47, PYGO1 demonstrates more restricted expression in normal tissues, which could mitigate off-target toxicity. Furthermore, targeting PYGO1 may simultaneously regulate both ITGB1-mediated invasion and CD47-dependent immune evasion pathways, potentially generating synergistic antitumor effects.

This study first reports a potential small-molecule inhibitor targeting PYGO1. Although no specific PYGO1 inhibitors have been reported to date, previous studies have identified lead compounds that block PYGO2-H3K4 me² interactions [39], providing important clues for developing epigenetic drugs targeting the PYGO family. We identified the active compound PGG from Paeonia lactiflora using computer-aided drug design, targeting the specific binding interface between the PYGO1-PHD domain and H3 K4 me^2/3 [21, 40]. The unique polyphenol structure of PGG enables it to exert antitumor effects through multiple targets [41, 42]. MD simulations revealed that PGG forms multivalent interactions with key amino acid residues (Val350, Gln354, Ala356, Ile357, and Tyr366) in the PYGO1-PHD, which are crucial for H3K4 me^2/3 recognition [20], thereby competitively inhibiting PYGO1 binding to H3K4 me^2/3. PGG treatment effectively inhibited GC cell proliferation, migration, and invasion while suppressing ITGB1/CD47 signaling, reproducing the effects of PYGO1 knockdown effects. For clinical translation, systematic evaluation of pharmacokinetics, long-term toxicity, and formulation optimization for PGG remains essential, with strategies such as polymeric nanomedicine development to enhance bioavailability and targeting specificity [43].

This study has several limitations. First, although we have demonstrated the critical role of PYGO1 in GC, its expression patterns and clinical relevance across different molecular subtypes require validation through multicenter, large-scale prospective studies. Second, mechanisms through which PYGO1 regulates metastasis and modulates the TIME remain unclear and should be investigated using more clinically relevant animal models and advanced coculture systems. Third, given the physiological importance of PYGO1 in the heart, assessment of tissue-specific toxicity using advanced models such as organ-on-a-chip technology will be crucial for developing PYGO1-targeted therapies for GC treatment.

Abbreviations

CD47

Cluster of differentiation 47

DEGs

Differentially expressed genes

DFS

Disease-free survival

ECM

Extracellular matrix

GES-1

Gastric epithelial cell line-1

GC

Gastric cancer

GEO

Gene Expression Omnibus

GSGC cohort

Gansu gastric cancer cohort

FCM

Flow cytometry

FBS

Fetal bovine serum

HE

Hematoxylin and Eosin

HR

Hazard ratio

H3 K4 me^2/3

Histone H3 lysine 4 di-/trimethylation

IC50

Half-maximal inhibitory concentration

IHC

Immunohistochemistry

IF

Immunofluorescence

IP

Immunoprecipitation

ITGB1

Integrin β1

LC–MS/MS

Liquid Chromatography-Tandem Mass Spectrometry

MD

Molecular dynamics

MMFF

Merck molecular force field

MS

Mass spectrometry

NHD

N-terminal homologous domain

OS

Overall survival

PBS

Phosphate-buffered saline

PGG

Pentagalloylglucose

PHD

Plant homologous domain

PYGO

Pygopus

qRT-PCR

Quantitative real-time polymerase chain reaction

Rg

Radius of gyration

RMSD

Root mean square deviation

RMSF

Root-mean-square fluctuation

RNA-seq

RNA sequencing

RT

Room temperature

scRNA-seq

Single-cell RNA sequencing

SD

Standard deviation

SIRPα

Signal regulatory protein α

TIME

Tumor immune microenvironment

TCGA-STAD

Stomach adenocarcinoma cohort of the cancer genome atlas

WB

Western blot

MFI

Mean fluorescence intensity

Author contributions

Conceptualization: Xiaoling Gao, Yanjuan Jia; Data Curation: Tao Qu, Zhenhao Li; Funding Acquisition: Yongqi Liu, Yanjuan Jia, Yaling Li; Investigation: Yanjuan Jia, Yaling Li, Yan Li; Methodology: Yanjuan Jia, Yaling Li, Zhuomin Fu, Yuanyuan Ma, Wanxia Wang, Miao Yu, Xiaojie Jin; Software: Yan Li; Visualization: Yanjuan Jia, Yan Li; Project Administration: Tao Qu, Yonghong Li; Supervision: Yonghong Li; Writing–Original Draft: Yanjuan Jia, Xiaoling Gao; Writing–Review & Editing: Xiaoling Gao, Yongqi Liu.

Funding

This research was supported by grants from the National Natural Science Foundation of China (Grant No. 82460554 to Yanjuan Jia); the Natural Science Foundation of Gansu Province (Grant No. 24JRRA875 to Yongqi Liu; Grant No. 23JRRA1279 to Yanjuan Jia); Gansu Provincial Hospital (Grant No. NHCDP2022019 to Yanjuan Jia); and the Gansu Postdoctoral Foundation (to Yaling Li).

Data availability

The data of the study are accessible within the article or from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

The study received ethical approval from the Ethics Committees of the Affiliated Hospital of Gansu University of Chinese Medicine (Approval No. [2023]18), and the Ethics Committee of Gansu Provincial Hospital (Approval No. 2022–155 and 2023–686), and informed consent was obtained from each patient. This study complied with the ethical guidelines of the Declaration of Helsinki. Animal experiments were approved by the Animal Experimental Ethical Inspection at Gansu University of Chinese Medicine (Approval No. 2023–571).

Consent for publication

All authors approved the manuscript for publication.

Competing interests

The authors disclose no competing of interests.